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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multifunctional Metal–Oxide Nanocomposite Thin Film with Plasmonic Au Nanopillars Embedded in Magnetic La 0.67 Sr 0.33 MnO 3 Matrix

Searching for multifunctional materials with tunable magnetic and optical properties has been a critical task toward the implementation of future integrated optical devices. Vertically aligned nanocomposite (VAN) thin films provide a unique platform for multifunctional material designs. In this work, a new metal–oxide VAN has been designed with plasmonic Au nanopillars embedded in a ferromagnetic La 0.67 Sr 0.33 MnO 3 (LSMO) matrix. Such Au–LSMO nanocomposite presents intriguing plasmon resonance in the visible range and magnetic anisotropy property, which are functionalized by the Au and LSMO phase, respectively. Furthermore, the vertically aligned nanostructure of metal and dielectric oxide results in the hyperbolic property for near-field electromagnetic wave manipulation. Such optical and magnetic response could be further tailored by tuning the composition of Au and LSMO phases.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effects of Quantum Dot Loading on the Radioluminescence Efficiency in Quantum-Dot-Embedded Composites

Nanoparticle-embedded plastic scintillators are an emerging technology for fast, large-area, high-resolution radiation detection and imaging. Here, this study investigates the properties of such composites, focusing on the effects of the quantum dot (QD) concentration on the radioluminescence (RL) intensity, spectra, and dynamics. Experiments using CdSe/CdS QDs in a polymer reveal a superlinear increase in RL with the QD concentration despite optical losses from inner filtering and interparticle interactions. When corrected for inner filtering, RL shows a quadratic concentration dependence, consistent with simple analytical models of improving the secondary electron capture. Practically, the benefits of high QD concentrations are muted by optical losses, but the findings apply to other systems with insulating hosts. In addition to manipulating emission for large effective Stokes shifts, future improvements may come from hosts with higher stopping power and better charge transport, which enable more effective funneling of excitations but without concomitant optical losses associated with high nanoparticle concentrations.

Auger recombination↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2019

Transition Metal Atoms Embedded in Graphene: How Nitrogen Doping Increases CO Oxidation Activity

We study 14 transition metals on pristine and N-doped graphene using density functional theory. For double vacancies, nitrogen doping increases the binding strength of harder transition metals to the support and reduces their oxygen affinity. Inversely, the oxygen affinity of softer metals increases. Since O 2 binding energies are correlated with the CO oxidation barrier in a volcano-like trend, doping also affects the activity of the single-atom catalyst. Among these systems, Fe atoms embedded in N-doped graphene are the most active CO oxidation catalysts. Here, these insights can be used to guide the synthesis of highly active oxidation catalysts from nonprecious metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗
DOE OSTI2020

Embedding PbS Quantum Dots (QDs) in Pb-Halide Perovskite Matrices: QD Surface Chemistry and Antisolvent Effects on QD Dispersion and Confinement Properties

Hybrid materials of metal chalcogenide colloidal quantum dots (QDs) embedded in metal halide perovskites (MHPs) have led to composites with synergistic properties. Here, we investigate how QD size, surface chemistry, and MHP film formation methods affect the resulting optoelectronic properties of QD/MHP “dot-in-matrix” systems. We monitor the QD absorption and photoluminescence throughout synthesis, ligand exchange, and transfer into the MHP ink, and we characterize the final QD/MHP films via electron microscopy and transient absorption. In addition, we are the first to globally map how PbS QDs are distributed on the micrometer scale within these dot-in-matrix systems, using three-dimensional (3D) tomography time-of-flight secondary ion mass spectrometry. The surface chemistry imparted during synthesis directly affects the optical properties of the dot-in-matrix composites. Pb-halide passivation leads to QD/MHP dot-in-matrix samples with optical properties that are well-described by a theoretical model, based on a Type I finite-barrier heterostructure between the PbS QD and the MHP matrix. Samples without Pb-halide passivation show complicated size-dependent behavior, indicating a transition from a Type I heterostructure between the PbS QD wells and MHP barriers for small-sized QDs to PbS QDs that are electronically decoupled from the MHP matrix for larger QDs. Furthermore, the choice in perovskite antisolvent crystallization method leads to a difference in the spatial QD distribution within the perovskite matrix, differences in carrier lifetime, and photoluminescence shifts of up to 180 meV for PbS in methylammonium lead iodide. This work establishes an understanding of such emerging synergistic systems relevant for technologies such as photovoltaics, infrared emitters and detectors, and other unexplored technological applications.

92 SC - Basic Energy Sciences (BES) (SC-22)↗
DOE OSTI2024

Low-Temperature Activation and Coupling of Methane on MgO Nanostructures Embedded in Cu 2 O/Cu(111)

Here, the efficient conversion of methane into valuable hydrocarbons such as ethane and ethylene at relatively low temperatures without deactivation issues is crucial for advancing sustainable energy solutions. Herein, AP-XPS and STM studies show that MgO nanostructures (0.2-0.5 nm wide, 0.4-0.6 Å high) embedded in a Cu 2 O/Cu(111) substrate activate methane at room temperature, mainly dissociating it into CH x (x = 2 or 3) and H adatoms, with minimal conversion to C adatoms. These MgO nanostructures in contact with Cu 2 O/Cu(111) exhibit unique reactivity, enabling C-C coupling into ethane and ethylene at 500 K, a significantly lower temperature than that required for bulk MgO catalysts (>700 K), with negligible carbon deposition and no deactivation. DFT calculations corroborate these experimental findings. The CH 4,gas → *CH 3 +*H reaction is a downhill process on MgO/Cu 2 O/Cu(111) surfaces. The activation of methane is facilitated by an electron transfer from copper to MgO and the existence of Mg and O atoms with a low coordination number in the oxide nanostructures. The formation of O-CH 3 and O-H bonds overcomes the energy necessary for the cleavage of a C-H bond in methane. DFT studies reveal that smaller Mg 2 O 2 model clusters provide stronger binding and lower activation barriers for C-H dissociation in CH 4 , while larger Mg 3 O 3 clusters promote C-C coupling due to weaker *CH 3 binding. All these results emphasize the importance of size when optimizing the catalytic performance of MgO nanostructures in the selective conversion of methane.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Synergistically Enhanced Performance of Ultrathin Nanostructured Silicon Solar Cells Embedded in Plasmonically Assisted, Multispectral Luminescent Waveguides

Ultrathin silicon solar cells fabricated by anisotropic wet chemical etching of single-crystalline wafer materials represent an attractive materials platform that could provide many advantages for realizing high-performance, low-cost photovoltaics. However, their intrinsically limited photovoltaic performance arising from insufficient absorption of low-energy photons demands careful design of light management to maximize the efficiency and preserve the cost-effectiveness of solar cells. Herein we present an integrated flexible solar module of ultrathin, nanostructured silicon solar cells capable of simultaneously exploiting spectral upconversion and downshifting in conjunction with multispectral luminescent waveguides and a nanostructured plasmonic reflector to compensate for their weak optical absorption and enhance their performance. The 8 $μ$m-thick silicon solar cells incorporating a hexagonally periodic nanostructured surface relief are surface-embedded in layered multispectral luminescent media containing organic dyes and NaYF 4 :Yb 3+ ,Er 3+ nanocrystals as downshifting and upconverting luminophores, respectively, via printing-enabled deterministic materials assembly. The ultrathin nanostructured silicon microcells in the composite luminescent waveguide exhibit strongly augmented photocurrent (~40.1 mA/cm 2 ) and energy conversion efficiency (~12.8%) than devices with only a single type of luminescent species, owing to the synergistic contributions from optical downshifting, plasmonically enhanced upconversion, and waveguided photon flux for optical concentration, where the short-circuit current density increased by ~13.6 mA/cm 2 compared with microcells in a nonluminescent medium on a plain silver reflector under a confined illumination.

36 MATERIALS SCIENCE↗
DOE OSTI2018

High-Density Ultra-small Clusters and Single-Atom Fe Sites Embedded in Graphitic Carbon Nitride (g-C 3 N 4 ) for Highly Efficient Catalytic Advanced Oxidation Processes

Ultra-small metal clusters have attracted great attention owing to their superior catalytic performance and extensive application in heterogeneous catalysis. However, the synthesis of high-density metal clusters is very challenging due to their facile aggregation. In this work, one-step pyrolysis was used to synthesize ultra-small clusters and single-atom Fe sites embedded in graphitic carbon nitride with high density (iron loading up to 18.2 wt %), evidenced by high-angle annular dark field-scanning transmission electron microscopy, X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and 57 Fe Mössbauer spectroscopy. The catalysts exhibit enhanced activity and stability in degrading various organic samples in advanced oxidation processes. The drastically increased metal site density and stability provide useful insights into the design and synthesis of cluster catalysts for practical application in catalytic oxidation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Revisiting Understanding of Electrochemical CO 2 Reduction on Cu(111): Competing Proton-Coupled Electron Transfer Reaction Mechanisms Revealed by Embedded Correlated Wavefunction Theory

Copper (Cu) electrodes, as the most efficacious of CO 2 reduction reaction (CO 2 RR) electrocatalysts, serve as prototypes for determining and validating reaction mechanisms associated with electrochemical CO 2 reduction to hydrocarbons. As in situ electrochemical mechanism determination by experiments is still out of reach, such mechanistic analysis typically is conducted using density functional theory (DFT). The semilocal exchange-correlation (XC) approximations most often used to model such catalysis unfortunately engender a basic error: predicting the wrong adsorption site for CO (a key CO 2 RR intermediate) on the most ubiquitous facet of Cu, namely, Cu(111). This longstanding inconsistency casts lingering doubt on previous DFT predictions of the attendant CO 2 RR kinetics. Here, we apply embedded correlated wavefunction (ECW) theory, which corrects XC functional error, to study the CO 2 RR on Cu(111) via both surface hydride (*H) transfer and proton-coupled electron transfer (PCET). We predict that adsorbed CO (*CO) reduces almost equally to two intermediates, namely, hydroxymethylidyne (*COH) and formyl (*CHO) at –0.9 V vs the RHE. In contrast, semilocal DFT approximations predict a strong preference for *COH. With increasing applied potential, the dominance of *COH (formed via potential-independent surface *H transfer) diminishes, switching to the competitive formation of both *CHO and *COH (both formed via potential-dependent PCET). Furthermore, our results also demonstrate the importance of including explicitly modeled solvent molecules in predicting electron-transfer barriers and reveal the pitfalls of overreliance on simple surface *H transfer models of reduction reactions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Mechanically Robust Bismuth-Embedded Carbon Microspheres for Ultrafast Charging and Ultrastable Sodium-Ion Batteries

Advancements in the development of fast-charging and long-lasting microstructured alloying anodes with high volumetric capacities are essential for enhancing the operational efficiency of sodium-ion batteries (SIBs). These anodes, however, face challenges such as declined cyclability and rate capability, primarily due to mechanical degradation reduced by significant volumetric changes (over 252%) and slow kinetics of sodium-ion storage. Herein, we introduce a novel anode design featuring densely packed bismuth (Bi) embedded within highly conductive carbon microspheres to overcome the aforementioned challenges. Remarkably, the high loading Bi anode within carbon microspheres with a high tap density of 2.59 g cm -3 possesses significant mechanical strength exceeding 590 MPa and limits volume swelling of only 10.9% post-sodiation. This anode demonstrates a high volumetric capacity (908.3 mAh cm -3 ), ultrafast chargeability (200 A g -1 , full charge/discharge in just 5.5 s), and outstanding cyclability over 12,000 cycles and maintains exceptional cycling stability even at -30 °C. The full cell paired with a Na 3 V 2 (PO 4 ) 3 cathode retains over 80% capacity after 600 cycles at 36 C, demonstrating a remarkable rate capability of 126 C (full charge/discharge in 28.6 s). Our comprehensive experimental evaluations and chemo-mechanical simulations shed light on the mechanisms underpinning the anode's superior performance. In conclusion, this development marks a significant advancement in the design of durable and fast-charging anodes for high-performance SIBs.

25 ENERGY STORAGE↗
DOE OSTI2017

Synergistic Enhancement of Electrocatalytic CO 2 Reduction with Gold Nanoparticles Embedded in Functional Graphene Nanoribbon Composite Electrodes

Regulating the complex environment accounting for the stability, selectivity, and activity of catalytic metal nanoparticle interfaces represents a challenge to heterogeneous catalyst design. Here in this paper, we demonstrate the intrinsic performance enhancement of a composite material composed of gold nanoparticles (AuNPs) embedded in a bottom-up synthesized graphene nanoribbon (GNR) matrix for the electrocatalytic reduction of CO 2 . Electrochemical studies reveal that the structural and electronic properties of the GNR composite matrix increase the AuNP electrochemically active surface area (ECSA), lower the requisite CO 2 reduction overpotential by hundreds of millivolts (catalytic onset > -0.2 V versus reversible hydrogen electrode (RHE)), increase the Faraday efficiency (>90%), markedly improve stability (catalytic performance sustained over >24 h), and increase the total catalytic output (>100-fold improvement over traditional amorphous carbon AuNP supports). The inherent structural and electronic tunability of bottom-up synthesized GNR-AuNP composites affords an unrivaled degree of control over the catalytic environment, providing a means for such profound effects as shifting the rate-determining step in the electrocatalytic reduction of CO 2 to CO, and thereby altering the electrocatalytic mechanism at the nanoparticle surface.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2022

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Abstract Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

All-silicon quantum light source by embedding an atomic emissive center in a nanophotonic cavity

Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stabilization of high-performance rock-salt LiMnSbTe3 thermoelectrics with embedded van der Waals-like gaps

Rock-salt-structured compounds like lead chalcogenides are promising thermoelectrics, as their high symmetry, strong anharmonicity, and favorable phase behavior collectively lead to high performance by enabling large power factors and ultralow thermal conductivity. Here, we report LiMnSbTe 3 , a new rock-salt semiconductor stabilized through targeted chemical design by combining hexagonal MnTe with cubic LiSbTe 2 . Embedded in the highsymmetry matrix, van der Waals-like gaps form due to Sb 2 Te 3 nanoscale segregation, which acts as effective phonon-scattering centers, leading to a low lattice thermal conductivity of 0.37Wm- 1 K -1 at 873 K with alloy scattering from disordered cations. The ordered local structure of Sb2Te 3 -type vdW-like gaps and the cross-gap interaction facilitate the carrier transport. Aided by energyconverged valence bands and a paramagnon drag effect, high Seebeck coefficients and enhanced power factor can be achieved, leading to a high ZT of 1.2 at 873 K. Furthermore, introducing Mn deficiency increases ZT to 1.5, highlighting the potential for higher performance through optimized doping or alloying. A segmented single-leg thermoelectric module achieves an output power density of 0.52 Wcm -2 and an efficiency of 8.7% under ΔT of 478 K, further demonstrating its promising thermoelectric applications.

42 ENGINEERING↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗